Funded by the EPSRC February 2011 -
September 2012 (£101k, EP/I003347/1)

Introduction

Unmanned surface vehicles (USVs) are
routinely being deployed in applications such as remote sensing,
surveillance, coast patrolling and providing navigation and
communication support to unmanned underwater vehicles (UUVs). In many
instances, they are remotely operated to perform a specific mission in
open or confined waters. The intelligence of these vehicles primarily
reside in the navigation, guidance and control (NGC) systems design.
Ideally, the vehicle needs to operate without any human intervention.
This means that the vessel's onboard control system must be self
reliant and able to maintain and supervise each onboard component.
Having said that, even with the most advanced NGC design, the craft
cannot be fully autonomous without the presence of an obstacle
detection and avoidance (ODA) system. Studies have shown that, in
manned vessels, more than 60% of casualties at sea are caused by
collisions. In addition, it has been found that human error is a major
contributing factor to those incidents. This could be due to an
ever-decreasing number of crew members adding more responsibilities
per person. For uninhabited surface craft, this cannot be overlooked
as their collision with other manned ships could endanger human lives.
Hence a human operator is always required to maintain a constant
lookout for any potential obstacles.

Aim and Objectives

The aim of this project is to design
and develop an ODA system primarily for an uninhabited marine craft.
The evasive manoeuvres will be based on marine 'rules of the road' or
collision regulations (COLREGs) on prevention of collision at sea
defined by the International Marine
Organisation. The ODA strategies
that will be developed herein will be directly addressing one of the
shortcomings of the current generation of unmanned surface vehicles,
however, it could also be employed in manned vessels and other land
based vehicles. The majority of the existing motion planning
strategies either ignore parameters such as ship dynamics,
environmental conditions and COLREGs or treat them on an ad-hoc basis.
It is envisaged that this project will bridge this gap by considering
these factors and thus automating this vital navigation component. In
order to achieve this, multi-objective optimisation will be employed
which will satisfy the criteria as specified above to determine a
feasible path. A vision-based obstacle detection algorithm together
with a laser range finder will also be investigated for close-range
encounters and integrated with the path planning module.

PROGRESS
REPORT AUGUST 2011

Work is steadily progressing in two distinct areas:

Path planning

1. A comprehensive literature review is being carried out by the PhD
student, Sable
Campbell.

2. 2 conference papers have been accepted in refereed international
conferences.

4. A talk was given at the Knowledge Transfer Network
Workshop (Aerospace, Aviation and Defence), Bridging the Autonomous
Divide, in London in May 2011. Details can be found here.

Obstacle Detection

1. A Post doctoral Research Assistant, Dr
Mamun Abu-Tair, commenced on July 1, 2011 to work on object
detection using a camera mounted on a pan and tilt platform combined
with a laser range sensor.

Dr Abu-Tair will develop video processing algorithms for obstacle
detection and classification in real time. A risk assessment
methodology will be developed to classify potential collisions and
integrated with path planning methods.

PROGRESS
REPORT JAN 2012

Good progress has been made on developing COLREGs-compliant path
planning algorithms. Currently, modified forms of A* are being
developed in addition to integrating the dynamics of various types of
vessels.

A
ship simulator package, 'Virtual Simulator' has been procured for
training and generating videos to be used for obstacle detection
purposes.

For obstacle detection, the hardware include a high-definition
camera in conjunction with a laser range sensor. Standard background
subtraction techniques along with Kalman filter based prediction
algorithms are employed to estimate the current position of the
obstacle and its projected position in the future.

Obstacle Detection and Identification System

A Collision Avoidance system for Unmanned Maritime Vehicles has been
designed and implemented. The proposed system employs a high
definition video camera and a laser range sensor mounted on a pan
and tilt device to detect obstacles in the vicinity of the ship. The
reasons behind employing the pan and tilt devices are (a) to
increase the coverage range and (b) to act as a stabiliser for the
high definition video camera and the laser sensor mounted on
it. Figure 1 shows the proposed system in operation.

Figure 1. The
proposed ODA system in operation

Additionally, different software based subsystems have been
implemented for the proposed solutions including Obstacle
Detection/identification, Risk Assessment Unit and prediction
subsystems. An extensive performance evaluation of the proposed
system in virtual maritime environments has been conducted. Figure 2
shows the obstacle detection and identification subsystem has
successfully detected the two cruisers present in the scene in
real-time.

Figure 2. Detecting multiple dynamic ships by the

obstacle
detection and identification subsystem

Figure 3 shows the captain radar screen. The figure illustrates
this scenario in which the risk assessment unit requests the pan and
tilt device to re-scan sector 2. The request can be seen at the top
of the virtual map and will be highlighted for the operator.

3.
Abu-Tair, M and Naeem, W, "A Decision Support Framework
for Collision Avoidance of Unmanned Maritime Vehicles",
Submitted to 9th IFAC Conference on Manueovring and Control of
Marine Craft, Arenzano, Italy, September 2012.

SUMMARY

The aim of this paper is to report the preliminary development of an automatic collision avoidance technique for unmanned marine craft based on standardised rules,
COLREGs, defined by the International Maritime Organisation. It is noted that all marine surface vessels are required to adhere to COLREGs at all times in order to minimise or eliminate the risk of collisions. The approach presented is essentially a reactive path planning algorithm which provides feedback to the autopilot of an unmanned vessel or the human captain of a manned ship for steering the craft safely. The proposed strategy consists of waypoint guidance by line-of-sight coupled with a manual biasing scheme. This is applied to the dynamic model of an unmanned surface vehicle. A simple PID autopilot is incorporated to ensure that the vessel adheres to the generated seaway. It is shown through simulations that the resulting scheme is able to generate viable trajectories in the presence of both stationary and dynamic obstacles. Rules 8 and 14 of the COLREGs, which apply to the amount of manoeuvre and to a head-on scenario respectively are simulated. A comparison is also made with an offline or deliberative grid-based path planning algorithm which has been modified to generate COLREGs-compliant routes.

Unmanned
surface vehicles (USVs) are routinely being deployed in
applications such as remote sensing, surveillance, coast
patrolling and providing navigation and communication support
to unmanned underwater vehicles (UUVs). In many instances,
they are remotely operated to perform a specific mission in
open or confined waters. The intelligence of these vehicles
primarily reside in the navigation, guidance and control (NGC)
systems design. Ideally, the vehicle needs to operate without
any human intervention. This means that the vessel's onboard
control system must be self reliant and able to maintain and
supervise each onboard component. Having said that, even with
the most advanced NGC design, the craft cannot be fully
autonomous without the presence of an obstacle detection and
avoidance (ODA) system. Studies have shown that, in manned
vessels, more than 60% of casualties at sea are caused by
collisions. In addition, it has been found that human error is
a major contributing factor to those incidents. This could be
due to an ever-decreasing number of crew members adding more
responsibilities per person. For uninhabited surface craft,
this cannot be overlooked as their collision with other manned
ships could endanger human lives. Hence a human operator is
always required to maintain a constant lookout for any
potential obstacles. The aim of this project is to design and
develop an ODA system primarily for an uninhabited marine
craft. The evasive manoeuvres will be based on marine 'rules
of the road' or collision regulations (COLREGs) on prevention
of collision at sea defined by the International Marine
Organisation. The ODA strategies that will be developed herein
will be directly addressing one of the shortcomings of the
current generation of unmanned surface vehicles, however, it
could also be employed in manned vessels and other land based
vehicles. The majority of the existing motion planning
strategies either ignore parameters such as ship dynamics,
environmental conditions and COLREGs or treat them on an
ad-hoc basis. It is envisaged that this project will bridge
this gap by considering these factors and thus automating this
vital navigation component. In order to achieve this,
multi-objective optimisation will be employed which will
satisfy the criteria as specified above to determine a
feasible path. A vision-based obstacle detection algorithm
together with a laser range finder will also be investigated
for close-range encounters and integrated with the path
planning module.

Key
Findings

-
Research in autonomous obstacle detection and motion planning
techniques for manned and unmanned marine vehicles has been
carried out which is of great use for the safety of marine
vessels.

- A COLREGs-compliant path planner (R-RA*) has been developed.
Although a number of path planning strategies exists but they
all need to be modified for COLREGs compliance for application
to marine vehicles.

- Training of PhD students as well as Research Fellow on
advanced autonomous systems in the field of unmanned vehicles
which will help to increase the skills of the workforce to
improve the competitiveness of UK maritime industry.

Potential
use in non-academic contexts

Hardware
and software developing during the course of this project has
the potential to be used by unmanned surface vehicles
manufacturers as the current available technolgoy is very
expensive.

The Bluefish SNAV
platform, presently under development, is a robotic ocean workhorse. Based on a stable
SWASH
hull that can achieve high speeds for long duration. This robot
ship uses no diesel fuel to monitor the oceans autonomously (COLREGS
compliant) at 6-7 knots continuously 24/7 and 365 days a year - only possible with the revolutionary (patent) energy harvesting system.
The vessel may also sprint from one location to another covering
distances in excess of 100 nautical mile at speeds of over 10 knots -
Thus may reach a target area within a relatively short response time. The
hullform is ideal for automatic release and recovery of ROVs
or towed arrays, alternating between drone and fully autonomous modes.
International development partners and agents are welcome. Initial
results suggest that this vessel
pays for itself in fuel saved every ten years.